This paper addresses the problem of designing signals for general group representations subject to constraints which are formulated as convex sets in the Hubert space of the group states. In particular, the paper cons...
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We develop an algorithm for adaptively estimating the noise subspace of a data matrix, as is required in signalprocessing applications employing the 'signal subspace' approach. The noise subspace is estimated...
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In this paper, we propose a new algorithm for computing a singular value decomposition of a matrix product. We show that our algorithm is numerically desirable in that all relevant residual elements will be numericall...
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In this paper, we propose a new algorithm for computing a singular value decomposition of a matrix product. We show that our algorithm is numerically desirable in that all relevant residual elements will be numerically small. Our algorithm can be extended to a product of a larger number of upper triangular matrices.
Effective signal detection and feature extraction in noisy environments generally depend on exploiting some knowledge of the signal. When the signal is exactly known, the matched filter is the optimum signal processin...
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CORDIC algorithms offer an attractive alternative to multiply-and-add based algorithms for the implementation of two-dimensional rotations, preserving either norm: (x2 + y2) 1/2 or (x2 - y2) 1/2 . Indeed these nor who...
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CORDIC algorithms offer an attractive alternative to multiply-and-add based algorithms for the implementation of two-dimensional rotations, preserving either norm: (x2 + y2) 1/2 or (x2 - y2) 1/2 . Indeed these nor whose computation is a significant part of the evaluation of the two-dimensional rotations, are computed much more easily by the CORDIC algorithms. However the part played by norm computations in the evaluation of rotations becomes quickly small as the dimension of the space increases. Thus, in spaces of dimension 5 or more, there is no practical alternative to multiply-and-add based algorithms. In the intermediate region, dimensions 3 and 4, extensions of the CORDIC algorithms are an interesting option. The four-dimensional extensions are particularly elegant and are the main object of this paper.
Beam-based adaptive processing is an economical way to achieve good interference rejection performance from an adaptive receiving array, at much less computational cost than full element-based methods. However, to exp...
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Beam-based adaptive processing is an economical way to achieve good interference rejection performance from an adaptive receiving array, at much less computational cost than full element-based methods. However, to exploit this potential for planar arrays it is necessary to identify, in real time, which beams must be retained for adaptive cancellation. This paper analyzes the beam-selection problem and presents a computationally efficient algorithm that performs real-time beam selection.
An iterative solution is given for solving deblurring problems having nonnegativity constraints through the use of methods motivated by tomographic imaging. After briefly reviewing three versions of tomographic-imagin...
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An iterative solution is given for solving deblurring problems having nonnegativity constraints through the use of methods motivated by tomographic imaging. After briefly reviewing three versions of tomographic-imaging problems, the paper indicates how methods that have proven to be powerful for the third version, weighted-integral tomography, can be applied to the more general deblurring problem when nonnegativity constraints are present.
In this paper, an adaptive algorithm for direction-finding of correlated sources is presented. The algorithm is low in computational complexity and it does not require determination of the effective rank of the array ...
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In this paper, an adaptive algorithm for direction-finding of correlated sources is presented. The algorithm is low in computational complexity and it does not require determination of the effective rank of the array correlation matrix. The algorithm employs a gradient technique to determine the minimum eigenvector of the correlation matrix and an orthogonalization technique to determine the second minimum eigenvector. The two noise eigenvectors are then used to compute the spatial spectra. The angle of arrivals can then be found by superimposing the two spectra. To verify further the true arrivals, additional spatial spectral can be computed using a combination of the two noise eigenvectors. Numerical results show that the proposed algorithms are effective in resolving correlated sources.
This conference proceedings contains 200 papers. The topics covered are neural networks and signalprocessing;heterojunction devices for high-speed signalprocessing, software prototypes in silicon compilation researc...
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This conference proceedings contains 200 papers. The topics covered are neural networks and signalprocessing;heterojunction devices for high-speed signalprocessing, software prototypes in silicon compilation research, parallel algorithms and architectures for DSP applications, topics in graph theory and engineering networks, topics in fault analysis, filter theory, telecommunication circuits, switched capacitor networks, graph theory and applications, DSP hardware systems, application of artificial neural networks to power systems, digital implementations of neural networks, analog implementations of neural networks multidimensional systems, VLSI for digital video signalprocessing, GaAs analog integrated circuits and system applications, adoptive systems, and solid state circuits and active filters.
Cumulants, and their associated Fourier transforms, known as polyspectra, are very useful in situations where one or more of the preceding phenomena - non-Gaussianity, nonminimum phase, colored Gaussian noise, and non...
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Cumulants, and their associated Fourier transforms, known as polyspectra, are very useful in situations where one or more of the preceding phenomena - non-Gaussianity, nonminimum phase, colored Gaussian noise, and nonlinearities - are present. Because second-order-based techniques have not led to very useful results in the face of these phenomena, it is no exaggeration to believe that it should be possible to reexamine every application and/or method that has ever made use of second-order statistics, using higher-order statistics, to see if better results can be obtained. The purpose of this paper is to give a brief introduction to cumulants and polyspectra and to give a brief overview of some of their applications.
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